{"id":"W4386642634","doi":"10.1061/jpsea2.pseng-1444","title":"Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ground-penetrating radar; Artificial intelligence; Radar; Remote sensing; Radar imaging; Computer science; Computer vision; Support vector machine; Geology; Mining engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001292183,0.0001053835,0.0002323668,0.0002342827,0.00004219624,0.00005686181,0.00004012217,0.00005251281,3.922538e-7],"category_scores_gemma":[0.0007322678,0.0001018555,0.00002578237,0.0003993903,0.00001119796,0.0003580025,0.0000123795,0.0003078711,5.538424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003949696,"about_ca_system_score_gemma":0.00001195361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009603308,"about_ca_topic_score_gemma":0.000005242888,"domain_scores_codex":[0.999059,0.0001162669,0.0004847312,0.00008088947,0.0001409594,0.0001181879],"domain_scores_gemma":[0.9984798,0.001076812,0.0002463969,0.00006488837,0.00008092565,0.00005113404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000807172,0.00001536527,0.0001393272,0.000753571,0.00003599982,0.000007488276,0.0003433785,0.1158903,0.8736269,0.0001344394,0.000002994804,0.009042104],"study_design_scores_gemma":[0.0002588088,0.00004874896,0.01091442,0.0002774302,0.00005142823,0.0003067766,0.001240892,0.9860172,0.0005050224,0.00003846144,0.0002400835,0.000100756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.919484,0.001670443,0.07833844,0.00004754337,0.000218958,0.0001024213,0.000001633511,0.00006733894,0.00006927466],"genre_scores_gemma":[0.9858297,0.0003323791,0.0136704,0.000001166451,0.0001288547,0.000003091106,0.000001036378,0.00002138583,0.00001199833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8731219,"threshold_uncertainty_score":0.4153547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02917950296822313,"score_gpt":0.2916267604459992,"score_spread":0.2624472574777761,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}